CN112882391B - Double-end event triggered nonlinear control method - Google Patents

Double-end event triggered nonlinear control method Download PDF

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CN112882391B
CN112882391B CN202110102587.7A CN202110102587A CN112882391B CN 112882391 B CN112882391 B CN 112882391B CN 202110102587 A CN202110102587 A CN 202110102587A CN 112882391 B CN112882391 B CN 112882391B
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赵涛
张坤朋
佃松宜
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Sichuan University
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Abstract

The invention discloses a double-end event-triggered nonlinear control method, wherein two self-adaptive event generators are respectively installed in network channels from a sensor to an observer and from the observer to a controller to ensure the smooth communication of a global network, an event trigger threshold value is obtained by utilizing a self-adaptive function related to the current state error, and a special time interval division method is adopted to unify the time-varying delay information of the network channels at the two sides of the observer under a frame; the method comprises the steps of providing an observer-based non-parallel distributed compensation fuzzy feedback controller to ensure the stability of a networked system, supplementing related information of double-end adaptive event triggering conditions in a Lyapunov function, obtaining the stability condition of the system by applying a Lyapunov theoretical analysis method, and developing a linear matrix inequality-based membership function related stability condition under an incomplete precondition matching condition to further obtain a parameter matrix of an observer and a controller in the system.

Description

Double-end event triggered nonlinear control method
Technical Field
The invention belongs to the technical field of event trigger mechanisms in network control systems, and particularly relates to a double-end event-triggered nonlinear control method.
Background
In recent years, due to the popularization of the internet, a network control system that is a combination of a conventional control method and a network has gradually become one of the major research hotspots in the control field. Different from the traditional control system structure, the network control system utilizes a network transmission channel to replace a complex wired connection to transmit control information and state information so as to simplify the physical complexity of communication and realize long-distance transmission, and has higher control efficiency and lower maintenance cost. However, when data is transmitted in a large scale, network congestion may occur and control performance may be affected due to the limitation of network equipment load. To solve this problem, an event triggering scheme has been proposed to improve the network transmission environment, which is periodically adopted and is characterized in that a necessary sampling signal is transmitted to reduce the network load only if an event triggering condition is satisfied, which makes it more desirable than periodic sampling. Generally, the structure and state of a system may be different with time, a conventional event triggering mechanism depends on a preset constant threshold, and an event triggering condition based on a stable threshold is obviously not an optimal choice, which cannot effectively reduce network congestion and ensure optimal system performance.
The adaptive event triggering mechanism can be adaptively adjusted according to the current network condition so as to effectively ensure the balance between the network load and the system performance, the control problem of a pure feedback object is solved by using the adaptive event triggering mechanism, and a wider system partial derivative condition is obtained. When network packet loss and actuator faults are considered, the waste of communication resources is reduced by providing an adaptive event triggering mechanism. In order to save computational resources effectively, a novel adaptive event triggering mechanism is proposed, which introduces an adaptive function related to the state error to adjust the threshold. Finally, the reliability of the proposed theory is proved by comparing with the simulated data transmission efficiency.
Due to the complex and nonlinear nature of the structure, networked control systems are often difficult to model and perform accurate mathematical analyses. The Takagi-Sugeno (T-S) model consists of a linear subsystem, provides a mathematical basis and theoretical guarantees for solving the problem, and is beneficial to deriving the stability condition and the controller of the nonlinear system. In general, due to the complex structure of the system, some states cannot be obtained by measurement. Therefore, observer-based controller design has become an active research focus for researchers. The conclusions provided by the observer-based controller above are based on a single-channel event-triggered approach, however, given the more complex engineering issues, there are situations where both the observer input and output signals need to be communicated over a network, so a good transmission environment must be created for both sensor-to-observer and observer-to-controller channels of data through a suitable event-triggering mechanism.
In recent years, the event trigger mechanism has become one of the important means for dealing with communication data redundancy in the networked control system, and is mainly developing towards two directions: one is to apply the event trigger mechanism to a more complex network environment, and the other is to improve the existing event trigger mechanism, so that the system can improve the data transmission efficiency when meeting the expected performance. The method considers that network channels are constructed at both ends of the observer, and under the framework of incomplete premise matching, the problems of observer-based fuzzy controller design and stability of a networked control system with a double-end adaptive event triggering mechanism are considered.
Disclosure of Invention
In order to overcome the defects in the prior art, the nonlinear control method triggered by the double-end event provided by the invention considers that network channels are constructed at both ends of the observer, and considers the problems of observer-based fuzzy controller design and stability of a networked control system with a double-end adaptive event triggering mechanism under the framework of incomplete premise matching.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that: a double-ended event triggered nonlinear control method comprises the following steps:
s1, constructing a nonlinear networked control system closed-loop model with a double-end event trigger mechanism;
s2, defining a Lyapunov function containing event triggering information, and determining the stability condition of a closed-loop model of the nonlinear networked control system by using the Lyapunov function;
and S3, solving parameter matrixes of an observer and a controller of the closed-loop model of the nonlinear networked control system through a linear matrix inequality based on the stability condition of the closed-loop model of the nonlinear networked control system, and further realizing nonlinear control.
The invention has the beneficial effects that:
1) the current discussion of event triggering mechanisms remains on single channel solutions. However, in order to meet the increasing engineering requirements, control signals sometimes need to be transmitted to the actuators through the network, so that it is of great significance to discuss a double-end event triggering mechanism for guaranteeing network smoothness of a global system. The invention provides a novel double-end self-adaptive event triggering scheme, wherein two self-adaptive event generators are respectively installed in a sensor-observer channel and an observer-controller channel to ensure smooth communication of a global network, and an event triggering threshold value is obtained by utilizing a self-adaptive function related to a current state error.
2) Because the introduction of the dual-end network can cause different time delays to be generated when communication data are transmitted in different network channels, time-varying time delay information of the network channels on two sides of the observer needs to be considered respectively, and for convenience of analysis, a special time interval division method is adopted to unify the time delay information under a framework.
3) The invention provides an observer-based non-parallel distributed compensation fuzzy feedback controller to ensure the stability of a networked system, relevant information of double-end adaptive event triggering conditions is supplemented in a Lyapunov function, the stability condition of the system is obtained by applying a Lyapunov theoretical analysis method, the stability condition related to a membership function based on a linear matrix inequality is developed under the incomplete precondition matching condition, a relaxation matrix with proper dimensionality is designed according to partial information of the membership function, and the conservatism of the proposed theoretical result is further reduced.
Drawings
Fig. 1 is a flowchart of a double-ended event triggered nonlinear control method provided by the present invention.
Fig. 2 is a structural block diagram of a closed-loop model of a nonlinear networked control system provided by the present invention.
Fig. 3 is a schematic structural diagram of a permanent magnet synchronous motor model in an embodiment of the present invention.
Fig. 4 is a schematic diagram of a system state x (t) in an embodiment of the present invention.
FIG. 5 shows systematic errors in an embodiment of the present invention
Figure BDA0002916185510000041
Schematic representation.
Fig. 6 is a schematic diagram of network transmission in a sensor-to-controller channel under an adaptive event triggering mechanism according to an embodiment of the present invention.
Fig. 7 is a schematic diagram of network transmission in an observer to controller channel under an adaptive event triggering mechanism according to an embodiment of the present invention.
Fig. 8 is a schematic diagram of network transmission in a sensor-to-controller channel under a conventional event trigger mechanism according to an embodiment of the present invention.
Fig. 9 is a schematic diagram of network transmission in an observer-to-controller channel under a conventional event trigger mechanism according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined by the appended claims, and all changes that can be made by the invention using the inventive concept are intended to be protected.
Example 1:
as shown in fig. 1, a double-ended event triggered nonlinear control method includes the following steps:
s1, constructing a nonlinear networked control system closed-loop model with a double-end event trigger mechanism;
s2, defining a Lyapunov function containing event triggering information, and determining the stability condition of a closed-loop model of the nonlinear networked control system by using the Lyapunov function;
and S3, solving parameter matrixes of an observer and a controller of the closed-loop model of the nonlinear networked control system through a linear matrix inequality based on the stability condition of the closed-loop model of the nonlinear networked control system, and further realizing nonlinear control.
As shown in fig. 2, the nonlinear networked control system closed-loop model in step S1 includes a controlled object, a sensor, an observer, a controller, and an actuator, which are sequentially connected in a closed-loop manner;
the output end of the controller is also connected with the input end of the observer;
to facilitate stability analysis, the controlled object in FIG. 2 is described by a T-S fuzzy model;
in order to reduce unnecessary data transmission, the observer is an observer with a double-end self-adaptive event trigger mechanism;
the controller is a fuzzy controller based on a non-parallel distributed compensation strategy.
In order to ensure the stability of the system, the controller is a fuzzy controller based on non-parallel distribution compensation measurement.
In this embodiment, for the controlled object, the T-S fuzzy model with r fuzzy rules is described as follows:
for the object rule i, when the controlled object blurs the precondition f1(x(t))…fκ(x (t)) the corresponding fuzzy sets are in turn
Figure BDA0002916185510000051
Then, obtaining:
Figure BDA0002916185510000052
in the formula, r is the number of fuzzy rules;
Figure BDA0002916185510000058
for fuzzy precondition variable fq(x (t)) a corresponding fuzzy set, where q 1, 2.. and k and i 1, 2.. and r, x (t) denote state vectors, u (t) denotes control input vectors, a (a) denotes control input vectors, and (b) denotes control input vectorsi，BiAnd CiAre all a matrix of the system, and,
Figure BDA0002916185510000053
is a state rate of change vector, y (t) is a system output vector,
Figure BDA0002916185510000054
Figure BDA0002916185510000055
for the purposes of an n-dimensional euclidean space,
Figure BDA0002916185510000056
Figure BDA0002916185510000057
is an m-dimensional euclidean space;
the global model corresponding to the controlled object is as follows:
Figure BDA0002916185510000061
in the formula, wi(x (t)) is degree of membership
Figure BDA0002916185510000062
Corresponding fuzzy weight in the controlled object;
Figure BDA0002916185510000063
membership function wi(x (t)) satisfies:
Figure BDA0002916185510000064
the observer with the double-end adaptive event triggering mechanism in this embodiment ensures smooth operation of network communication and improves network transmission efficiency, and in order to simplify analysis, the observer in this embodiment is described in the following manner:
for observer rule j, when observer blurs the preconditions
Figure BDA0002916185510000065
The corresponding fuzzy sets are sequentially
Figure BDA0002916185510000066
Then, obtaining:
Figure BDA0002916185510000067
in the formula (I), the compound is shown in the specification,
Figure BDA0002916185510000068
in order to observe the rate of change of state vector,
Figure BDA0002916185510000069
is a state vector of an observer, an
Figure BDA00029161855100000610
u (t) is a control input vector, observer rule j satisfies j is more than or equal to 2 and less than or equal to r,
Figure BDA00029161855100000611
is a fuzzy precondition of the observer
Figure BDA00029161855100000612
A corresponding fuzzy set, q 1,2jAs observer parameters, Aj,BjAnd CjAre all a matrix of the system, and,
Figure BDA00029161855100000613
is the output vector through the first event generator;
the global model corresponding to the observer is as follows:
Figure BDA00029161855100000614
in the formula (I), the compound is shown in the specification,
Figure BDA00029161855100000615
is degree of membership
Figure BDA00029161855100000616
A corresponding fuzzy weight in the observer;
in this embodiment, in order to make the structural design of the controller more flexible, we consider an independent precondition variable in the fuzzy rule so as to optimize the structural design of the control, the controller is a fuzzy controller based on a non-parallel distribution compensation strategy, and the description mode of the controller is as follows:
for controller rule l, when the controller fuzzes the precondition variables
Figure BDA0002916185510000071
The corresponding fuzzy sets are sequentially
Figure BDA0002916185510000072
Then, obtaining:
Figure BDA0002916185510000073
wherein u (t) is a control input vector,
Figure BDA0002916185510000074
fuzzy precondition variables for a controller
Figure BDA0002916185510000075
Corresponding fuzzy sets, q 1,2, p, l 1,2, r, p is the number of fuzzy controller preconditions, Kl is a fuzzy controller parameter matrix,
Figure BDA0002916185510000076
inputting a state vector for a controller;
the global model corresponding to the controller is as follows:
Figure BDA0002916185510000077
Figure BDA0002916185510000078
in the formula (I), the compound is shown in the specification,
Figure BDA0002916185510000079
is the fuzzy weight of the fuzzy controller;
Figure BDA00029161855100000710
the error of the observer is:
Figure BDA00029161855100000711
based on the controlled object, the observer and the global models (2), (5) and (10) corresponding to the controller, the closed-loop model of the nonlinear network control system is obtained as follows:
Figure BDA00029161855100000712
in the formula,
Figure BDA00029161855100000714
to observe the coupling vectors for the rate of change of state and the state error,
Figure BDA00029161855100000715
Figure BDA00029161855100000713
therein, Ψijl、Aijl、Lijl、LeijlAnd LeyjIntermediate parameters in a closed-loop model of the nonlinear network control system are controlled.
To simplify the coincidence representation, in the following description we use ωi(x(t))＝ωi(x)，
Figure BDA0002916185510000081
Figure BDA0002916185510000082
In the embodiment, in a closed-loop model of a nonlinear networked control system, under an adaptive event triggering mechanism, a first event generator is arranged in a data transmission channel from a sensor to an observer, and the first event generator can reduce network redundancy signals;
in the sensor-to-observer data path, at event trigger times
Figure BDA0002916185510000083
System output signal transmitted by triggering first event generator through sensor end event
Figure BDA0002916185510000084
At the moment of time
Figure BDA0002916185510000085
The data center of the observer is reached through a transmission network; wherein the content of the first and second substances,
Figure BDA0002916185510000086
is a time of day
Figure BDA0002916185510000087
The network at the sensor end of (1) induces a time delay,
Figure BDA0002916185510000088
τyis a time of day
Figure BDA0002916185510000089
The maximum network induced delay of the sensor end;
the adaptive event trigger mechanism determines the current signal based on adaptive event trigger conditions
Figure BDA00029161855100000810
Whether or not to be transmitted to the observer, the next event triggering instant when the adaptive event triggering condition is satisfied
Figure BDA00029161855100000811
Comprises the following steps:
Figure BDA00029161855100000812
in the formula (I), the compound is shown in the specification,
Figure BDA00029161855100000813
j is the number of consecutive packets that occur for jh sets that satisfy the condition,
Figure BDA00029161855100000814
Figure BDA00029161855100000815
for the maximum allowable amount of data packet loss to continuously occur,
Figure BDA00029161855100000816
is the difference between the current output signal and the last trigger time, ΩyIs an event trigger matrix, and Ωy>0，ey(T) is the error, χ, between the current output signal of the T-S fuzzy model and the last transmitted output signaly(t) is satisfying xy(t)∈(0,1]Variable trigger threshold of yT() means for outputting a vector for the current system, y (-) is the current system output vector;
wherein the error e of the current output signal of the T-S fuzzy model and the last transmitted output signaly(t) is:
Figure BDA00029161855100000817
variable trigger threshold χy(t) satisfies:
Figure BDA00029161855100000818
wherein the content of the first and second substances,
Figure BDA00029161855100000819
in order for the event to trigger the threshold value,
Figure BDA00029161855100000820
for adjusting the variable trigger threshold χy(t) a parameter of convergence speed; therefore, the event trigger condition can be dynamically adjusted through the output signal of the system;
considering network induced delay, based on the characteristics of an adaptive event trigger mechanism, the input signal of an observer
Figure BDA0002916185510000091
Comprises the following steps:
Figure BDA0002916185510000092
based on the adaptive event triggering mechanism condition, the input signal of the observer in two adjacent triggering instant intervals is a constant, which effectively saves the occupation of communication resources, and for the convenience of analysis, the time interval with network-induced delay
Figure BDA0002916185510000093
Is divided into:
Figure BDA0002916185510000094
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0002916185510000095
in order to be able to process the time interval,
Figure BDA0002916185510000096
Figure BDA0002916185510000097
defining a time-varying function d (t):
Figure BDA0002916185510000098
Figure BDA0002916185510000099
therefore, the observer input signal based on the adaptive event triggering mechanism is:
Figure BDA00029161855100000910
wherein d (t) is time-varyingThe function of the function is that of the function,
Figure BDA00029161855100000911
Figure BDA00029161855100000912
in order to be able to process the time interval,
Figure BDA00029161855100000913
epsilon is an intermediate parameter which is,
Figure BDA00029161855100000914
tau is the maximum network-induced time delay,
Figure BDA00029161855100000915
in the embodiment, a second event generator is arranged in a data transmission channel from the observer to the controller;
in the data transmission channel from the observer to the controller, at the moment of application of the trigger
Figure BDA00029161855100000916
Observer state signal transmitted by observer-end data trigger transmitter
Figure BDA00029161855100000917
At the time of day
Figure BDA00029161855100000918
The data center of the controller is reached through a transmission network;
Figure BDA00029161855100000919
is a time of day
Figure BDA00029161855100000920
The maximum network at the observer end of (1) induces a time delay,
Figure BDA00029161855100000921
Figure BDA00029161855100000922
is a time of day
Figure BDA0002916185510000101
The maximum network induced delay of the observer end;
the second event generator reduces the network redundancy of the state signal under the condition of an adaptive event triggering mechanism according to the state information of the observer, wherein the condition of the adaptive triggering mechanism is as follows:
Figure BDA0002916185510000102
in the formula (I), the compound is shown in the specification,
Figure BDA0002916185510000103
for the moment when the next event trigger occurs,
Figure BDA0002916185510000104
as a transpose of the difference of the current observer state and the last triggered observer state,
Figure BDA0002916185510000105
in order for the event to trigger the matrix,
Figure BDA0002916185510000106
the difference between the current observer state and the last triggered observer state,
Figure BDA0002916185510000107
to adapt the trigger threshold of the trigger mechanism,
Figure BDA0002916185510000108
is a transpose of the state vector of the current observer,
Figure BDA0002916185510000109
is the current observer state vector;
wherein the trigger threshold of the adaptive trigger mechanism
Figure BDA00029161855100001010
Satisfies the following conditions:
Figure BDA00029161855100001011
in the formula (I), the compound is shown in the specification,
Figure BDA00029161855100001012
in order for the threshold to be triggered by an event,
Figure BDA00029161855100001013
for adjusting trigger threshold
Figure BDA00029161855100001014
A parameter of convergence speed; input signal of controller based on adaptive event trigger mechanism
Figure BDA00029161855100001015
Comprises the following steps:
Figure BDA00029161855100001016
to facilitate the analysis of asynchronous information of the sensor-to-observer and observer-to-controller channels, the subintervals of the two network channel transmission times need to be unified within one framework,
Figure BDA00029161855100001017
indicates at the current sampling instant is
Figure BDA00029161855100001018
Last event triggering instant of time observer to transmitter in controller channel
Figure BDA00029161855100001019
Difference between present status signal and last transmitted status signal
Figure BDA00029161855100001020
Comprises the following steps:
Figure BDA00029161855100001021
the same method as the division of the time interval from the sensor to the observer channel, the time interval
Figure BDA00029161855100001022
It can also be divided into time sets in units of time length h, resulting in:
Figure BDA00029161855100001023
thus, the time interval between two adjacent transmitters in the observer to controller can be expressed as:
Figure BDA0002916185510000111
thus, equation (22) can be converted to:
Figure BDA0002916185510000112
in summary, the input signals of the controllers based on the adaptive event triggering mechanisms according to (16), (19) and (23)
Figure BDA0002916185510000113
Comprises the following steps:
Figure BDA0002916185510000114
it should be noted that the maximum network-induced delay of the dual-end communication network in this embodiment is defined as
Figure BDA0002916185510000115
The data center at the sensor end is supposed to detect whether the buffer of the data center needs to be updated at each moment ih + tau, (i e N), and the data center at the observer end is supposed to detect whether the buffer of the data center needs to be updated at each moment ih + tau, (i e N); furthermore, we ignore the computation time required to update the data. By the method, asynchronous network time delays of a sensor-observer channel and an observer-controller channel can be unified in a framework so as to analyze the stability of the system.
Step S2 of this embodiment specifically includes:
s21, defining a Lyapunov function containing event trigger information;
s22, carrying out derivation on the Lyapunov function, and eliminating an integral term in the Lyapunov function to obtain the stability condition of the closed-loop model of the nonlinear networked control system;
s23, eliminating a nonlinear term in the stability condition of the nonlinear networked control system closed-loop model;
and S24, on the basis of the stability condition of the networked control system closed-loop model with the nonlinear term eliminated, obtaining a system stability condition related to the membership function based on the linear matrix inequality by using the time delay information of the membership function, and taking the system stability condition as the final stability condition of the nonlinear networked control system closed-loop model.
In the above step S22, dM>0，
Figure BDA0002916185510000116
And
Figure BDA0002916185510000117
if there is a matrix P with suitable dimensions>0，Q>0，R>0，W>0，Ωy>0，
Figure BDA0002916185510000118
And U, the stability condition of the closed-loop model of the nonlinear networked control system is as follows:
Figure BDA0002916185510000121
Figure BDA0002916185510000122
in the formula phiijlIn order to determine the matrix for the stability,
Figure BDA0002916185510000123
and
Figure BDA0002916185510000124
are all phiijlIs an element of
Figure BDA0002916185510000125
A transposed matrix of mid-diagonal positions;
wherein:
Figure BDA0002916185510000126
Figure BDA0002916185510000127
Figure BDA0002916185510000128
F＝[Aijl L ijl 0 Leijl Leyj]
in the formula (I), the compound is shown in the specification,
Figure BDA0002916185510000129
and
Figure BDA00029161855100001210
are all made of
Figure BDA00029161855100001211
Element of (1), col { ·The matrix is a column matrix, the diag { · } is a block diagonal matrix, and F is an auxiliary matrix;
and is
Figure BDA00029161855100001212
Figure BDA00029161855100001213
Figure BDA00029161855100001214
Figure BDA00029161855100001215
Figure BDA00029161855100001216
In the formula, G ═ I I, E ═ I0, and I is a unit matrix with an appropriate dimension.
The proof process that the system can be kept stable using the above-described stability condition based on the lyapunov function in step S21 is:
the Lyapunov function is chosen to be:
V(t)＝V1(t)+V2(t)+V3(t) (29)
in the formula (I), the compound is shown in the specification,
Figure BDA0002916185510000131
Figure BDA0002916185510000132
Figure BDA0002916185510000133
wherein ξT(t) is the transpose of the coupling vector for observed state change rate and state error, ξ (t) is the coupling vector for observed state change rate and state error, P, Q, R and W are both matrices with appropriate dimensions, ikh is an intermediate parameter which is a function of,
Figure BDA0002916185510000134
dMis the upper delay bound.
Obtained by derivation of the aforementioned lyapunov function (29):
Figure BDA0002916185510000135
in the formula (I), the compound is shown in the specification,
Figure BDA0002916185510000136
Figure BDA0002916185510000137
Figure BDA0002916185510000138
considering a network communication protocol (12) of the sensor-to-observer channel and an adaptive function (14) of the trigger threshold, we obtain:
Figure BDA0002916185510000139
taking into account the network communication protocol (19) of the observer to controller channel and the adaptive function (20) of the trigger threshold, we obtain:
Figure BDA0002916185510000141
based on theorem 1, obtaining
Figure BDA0002916185510000142
Wherein:
Figure BDA0002916185510000143
by combining (29) to (31), we can obtain:
Figure BDA0002916185510000144
get
Figure BDA0002916185510000145
The following:
Figure BDA0002916185510000146
Figure BDA0002916185510000147
Figure BDA0002916185510000148
according to the Shu's complement theory, the following results are obtained:
Figure BDA0002916185510000149
therefore, if there is Φijl<0, then the system (11) is asymptotically stable.
Because the existence of the nonlinear term, the stability condition cannot obtain a specific observer parameter matrix and a specific controller parameter matrix, so to solve these parameters, we obtain the system stability condition related to the membership function based on the linear matrix inequality in step S24 by using a linear matrix inequality method, where the stability condition is:
when d isM>0，
Figure BDA0002916185510000151
And
Figure BDA0002916185510000152
and the membership function of the fuzzy controller satisfies
Figure BDA0002916185510000153
Wherein 0<ιl≤1，ιlFor appropriate constants, if there is a matrix X with suitable dimensions>0，
Figure BDA0002916185510000154
Figure BDA0002916185510000155
TjAnd YlThen, the system stability condition related to the membership function based on the linear matrix inequality is:
Figure BDA0002916185510000156
Figure BDA0002916185510000157
Figure BDA0002916185510000158
Figure BDA0002916185510000159
wherein
Figure BDA00029161855100001510
Figure BDA00029161855100001511
Figure BDA00029161855100001512
Figure BDA00029161855100001513
Figure BDA00029161855100001514
Figure BDA0002916185510000161
Figure BDA0002916185510000162
Figure BDA0002916185510000163
Figure BDA0002916185510000164
Figure BDA0002916185510000165
Figure BDA0002916185510000166
Figure BDA0002916185510000167
Figure BDA0002916185510000168
Figure BDA0002916185510000169
Figure BDA00029161855100001610
Figure BDA00029161855100001611
Figure BDA00029161855100001612
Figure BDA00029161855100001613
Figure BDA00029161855100001614
Figure BDA00029161855100001615
Figure BDA00029161855100001616
Figure BDA00029161855100001617
Figure BDA00029161855100001618
Figure BDA00029161855100001619
Figure BDA00029161855100001620
In the formula (I), the compound is shown in the specification,
Figure BDA0002916185510000171
and
Figure BDA0002916185510000172
is composed of
Figure BDA0002916185510000173
Of (1).
The stability condition can keep the system stable, and the proving process for determining the parameter matrix of the controller and the parameter matrix of the observer is specifically as follows:
order to
Figure BDA0002916185510000174
According to theorem 2, if there is one
Figure BDA0002916185510000175
Can ensure that
Figure BDA0002916185510000176
Definition of
Figure BDA0002916185510000177
Figure BDA0002916185510000178
Figure BDA0002916185510000179
Figure BDA00029161855100001710
Yl＝KlX,
Figure BDA00029161855100001711
Multiplying the matrix T on the left and right sides of (28) to obtain
Figure BDA00029161855100001712
Wherein
Figure BDA00029161855100001713
To linearize
Figure BDA00029161855100001714
According to the literature[38]We take the following measures.
Due to the fact that
Figure BDA00029161855100001715
Figure BDA00029161855100001716
To obtain
Figure BDA00029161855100001717
Figure BDA00029161855100001718
In summary, (27) and (28) can be
Figure BDA00029161855100001719
And
Figure BDA00029161855100001720
and (5) ensuring.
Thus, we can get:
Figure BDA0002916185510000181
giving relaxation matrices of suitable dimensions
Figure BDA0002916185510000182
And the matrix satisfies
Figure BDA0002916185510000183
Figure BDA0002916185510000184
Thus, the following results:
Figure BDA0002916185510000185
if there are
Figure BDA0002916185510000186
Inequalities (34) - (37) can ensure
Figure BDA0002916185510000187
The system (11) is stable.
It should be noted that we use
Figure BDA0002916185510000188
Simplify
Figure BDA0002916185510000189
And, the matrix X is,
Figure BDA00029161855100001810
and T are both well-designed to linearize the nonlinear term in theory 2. In order to reduce the conservatism of the derived result, the stability condition of the linear matrix inequality is obtained by considering the boundary conditions of the membership functions of the fuzzy observer and the fuzzy controller.
Theorem 1 and theorem 2 used in this embodiment are respectively:
theorem 1: considering satisfaction of conditions
Figure BDA00029161855100001811
τ (t) of (1). For any satisfied condition
Figure BDA00029161855100001812
Of (2) matrix
Figure BDA0002916185510000191
And U ∈ Rn×nThe following equations are all true:
Figure BDA0002916185510000192
theorem 2: for a full rank matrix rank (c) m,
Figure BDA0002916185510000193
the singular value decomposition of C can be described as C ═ O [ S0 ═ O]VTWherein O.OTIs equal to I and V.VTI. Let matrix X>0，
Figure BDA0002916185510000194
And
Figure BDA0002916185510000195
if present
Figure BDA0002916185510000196
Satisfy the requirements of
Figure BDA0002916185510000197
Then the following holds:
Figure BDA0002916185510000198
wherein X >0 represents a true symmetric positive definite matrix. I denotes an identity matrix of a suitable dimension
The parameter matrix of the observer in step S3 is obtained based on the stability condition as:
Figure BDA0002916185510000199
the parameter matrix of the controller is:
Kl＝YlX-1
in the formula (I), the compound is shown in the specification,
Figure BDA00029161855100001910
example 2:
this embodiment provides a simulation example based on the method in embodiment 1 above:
as shown in fig. 3, the permanent magnet synchronous motor converts the feedback current signal by using a rotor reference system, and a mathematical model of the permanent magnet synchronous motor is expressed as follows:
Figure BDA00029161855100001911
Figure BDA00029161855100001912
in the formula, vmAnd vnRepresenting the voltage in the reference frame of the rotor, imAnd inIn the rotor reference systemCurrent, omegasThe motor speed is shown, and the rest physical parameters refer to the table 1.
To simplify the design of the control rate, assume that the m-axis current is 0. Then, the electromagnetic torque is adjusted using the n-axis current, which can be expressed as follows:
Figure BDA0002916185510000201
the dynamic equations can be expressed as follows:
Figure BDA0002916185510000202
Figure BDA0002916185510000203
Figure BDA0002916185510000204
wherein T isLRepresenting the input torque, the remaining physical parameters refer to table 1.
Table 1: model parameters of permanent magnet synchronous motor
Figure BDA0002916185510000205
In order to simplify the problem, the influence of external interference on the permanent magnet synchronous motor is ignored. And order states
Figure BDA0002916185510000206
The permanent magnet synchronous motor system can be modeled as a two-rule T-S fuzzy system:
system rule 1: if x1(t) is M1, then
Figure BDA0002916185510000207
System rule 2: if x1(t) is M2Then, then
Figure BDA0002916185510000208
Wherein:
Figure BDA0002916185510000211
the membership function is:
Figure BDA0002916185510000212
wherein the content of the first and second substances,
Figure BDA0002916185510000213
represents omegasIs determined by the minimum estimate of (c) of,
Figure BDA0002916185510000214
represents omega s2, d2 is 10 and ω is the maximum estimate of (d 1) ═ 2, d2 ═ 10s∈[d1,d2]。
Order to
Figure BDA0002916185510000215
And dM 0.07, the following parameter matrix can be obtained according to theory 2 (resulting in system stability conditions related to membership functions based on linear matrix inequalities):
Ωy＝8.9353
Figure BDA0002916185510000216
L1＝[-3.6755 0.0989 0.0171]T
L2＝[-1.7078 0.0124 0.0480]T
Figure BDA0002916185510000217
Figure BDA0002916185510000218
let the sampling period h equal to 0.02, initial state x0＝[10.5 -9.1 0.7]TAnd an initial threshold value
Figure BDA0002916185510000219
By MATLAB simulation, the permanent magnet synchronous motor can be effectively controlled, and network communication resources are saved. FIG. 4 shows that the state of the system can converge quickly to the origin; FIG. 5 illustrates state errors of the observer and system; FIGS. 6 and 7 show event triggering scenarios in the sensor-observer and observer-controller channels, respectively; FIGS. 8 and 9 illustrate the use of a conventional event trigger mechanism and event trigger threshold σ, respectivelyy＝0.2,
Figure BDA00029161855100002110
Event triggering conditions in a sensor-to-observer channel and an observer-to-controller channel; tables 2 and 3 list the number of network communication data transmissions under different communication schemes. Wherein table 2 lists the amount of data transferred from the sensor to the observer channel and table 3 lists the amount of data transferred from the observer to the controller channel.
Table 2: data volume transmitted under different communication schemes in sensor-observer channel
Figure BDA0002916185510000221
Table 3: data volume transmission under different communication schemes in observer-controller channel
Figure BDA0002916185510000222

Claims (7)

1. A double-end event triggered nonlinear control method is characterized by comprising the following steps:
s1, constructing a nonlinear networked control system closed-loop model with a double-end event trigger mechanism;
s2, defining a Lyapunov function containing event triggering information, and determining the stability condition of a closed-loop model of the nonlinear networked control system by using the Lyapunov function;
s3, solving parameter matrixes of an observer and a controller of the nonlinear networked control system closed-loop model through a linear matrix inequality based on the stability condition of the nonlinear networked control system closed-loop model, and further realizing nonlinear control;
the nonlinear networked control system closed-loop model in the step S1 includes a controlled object, a sensor, an observer, a controller and an actuator, which are sequentially connected in a closed-loop manner;
the output end of the controller is also connected with the input end of the observer;
the controlled object is described by a T-S fuzzy model;
the observer is an observer with a double-end self-adaptive event trigger mechanism;
the controller is a fuzzy controller based on a non-parallel distribution compensation strategy;
the T-S fuzzy model with r fuzzy rules is described in the following manner:
for the object rule i, when the controlled object blurs the precondition variable f1(x(t))…fκ(x (t)) the corresponding fuzzy sets are sequentially
Figure FDA0003570880930000011
Then, obtaining:
Figure FDA0003570880930000012
in the formula, r is the number of fuzzy rules;
Figure FDA0003570880930000018
for fuzzy precondition variable fq(x (t)) corresponding fuzzy sets, where q ═ 1, 2., κ and i ═ 1, 2., r, x (t) denote state vectors, u (t) denote control input vectors, a (a) denotes control input vectors, a (t) denotes fuzzy sets, and q ═ 1, 2., κ denotes control input vectorsi，BiAnd CiAre all a matrix of the system, and,
Figure FDA0003570880930000013
is a state rate of change vector, y (t) is a system output vector,
Figure FDA0003570880930000014
Figure FDA0003570880930000015
for the purposes of an n-dimensional euclidean space,
Figure FDA0003570880930000016
Figure FDA0003570880930000017
is an m-dimensional euclidean space;
the global model corresponding to the controlled object is as follows:
Figure FDA0003570880930000021
in the formula, wi(x (t)) is degree of membership
Figure FDA0003570880930000022
Corresponding fuzzy weight in the controlled object;
the description mode of the observer is as follows:
for observer rule j, when observer blurs precondition variable
Figure FDA0003570880930000023
Corresponding fuzzy set dependencesIs next to
Figure FDA0003570880930000024
Then, obtaining:
Figure FDA0003570880930000025
in the formula (I), the compound is shown in the specification,
Figure FDA0003570880930000026
in order to observe the rate of change of state vector,
Figure FDA0003570880930000027
is a state vector of an observer, an
Figure FDA0003570880930000028
u (t) is a control input vector, observer rule j satisfies j is more than or equal to 2 and less than or equal to r,
Figure FDA0003570880930000029
is a fuzzy precondition of the observer
Figure FDA00035708809300000210
A corresponding fuzzy set, q 1,2jAs observer parameters, Aj,BjAnd CjAre all a matrix of the system, and,
Figure FDA00035708809300000211
an input signal of an observer being an adaptive event triggering mechanism;
the global model corresponding to the observer is as follows:
Figure FDA00035708809300000212
in the formula (I), the compound is shown in the specification,
Figure FDA00035708809300000213
is degree of membership
Figure FDA00035708809300000214
A corresponding fuzzy weight in the observer;
the controller is described in the following manner:
for controller rule l, when the controller fuzzes the precondition variables
Figure FDA00035708809300000215
The corresponding fuzzy sets are sequentially
Figure FDA00035708809300000216
Then, obtaining:
Figure FDA00035708809300000217
wherein u (t) is a control input vector,
Figure FDA00035708809300000218
fuzzy precondition variables for a controller
Figure FDA00035708809300000219
A corresponding fuzzy set, q 1, 2., p, l 1, 2., r, p is the number of fuzzy preconditions of the controller, KlIn order to be a matrix of fuzzy controller parameters,
Figure FDA00035708809300000220
inputting a state vector for a controller;
the global model corresponding to the controller is as follows:
Figure FDA00035708809300000221
Figure FDA0003570880930000031
in the formula (I), the compound is shown in the specification,
Figure FDA0003570880930000032
is the fuzzy weight of the fuzzy controller;
based on the controlled object, the observer and the global model corresponding to the controller, the closed-loop model of the nonlinear network control system is obtained as follows:
Figure FDA0003570880930000033
in the formula (I), the compound is shown in the specification,
Figure FDA0003570880930000034
to observe the coupling vectors for the rate of change of state and the state error,
Figure FDA00035708809300000317
Figure FDA0003570880930000035
Figure FDA0003570880930000036
is the difference between the current observer state and the last observer state triggered, ey(T) is the error of the current output signal of the T-S fuzzy model and the last transmitted output signal;
therein, Ψijl、Aijl、Lijl、LeijlAnd LeyjIntermediate parameters in a closed-loop model of the nonlinear network control system are controlled.
2. The double-ended event triggered nonlinear control method according to claim 1, characterized in that in a nonlinear networked control system closed-loop model, a first event generator is provided in a sensor-to-observer data transmission channel under an adaptive event triggering mechanism;
in the sensor-to-observer data path, at event trigger times
Figure FDA0003570880930000037
System output signal transmitted by first event generator at sensor end
Figure FDA0003570880930000038
At the time of day
Figure FDA0003570880930000039
The data center of the observer is reached through a transmission network; wherein the content of the first and second substances,
Figure FDA00035708809300000310
is a time of day
Figure FDA00035708809300000311
The network at the sensor end of (1) induces a time delay,
Figure FDA00035708809300000312
τyis a time of day
Figure FDA00035708809300000313
The maximum network induced delay of the sensor end;
the adaptive event trigger mechanism determines the current signal based on adaptive event trigger conditions
Figure FDA00035708809300000314
Whether or not to be transmitted to the observer, the next event triggering instant when the adaptive event triggering condition is met
Figure FDA00035708809300000315
Comprises the following steps:
Figure FDA00035708809300000316
in the formula (I), the compound is shown in the specification,
Figure FDA0003570880930000041
to satisfy the condition
Figure FDA0003570880930000042
The set of (a) and (b),
Figure FDA0003570880930000043
in order for the number of data lost packets to occur continuously,
Figure FDA0003570880930000044
in order for the maximum allowable amount of data packet loss to continuously occur,
Figure FDA0003570880930000045
for the transposition of the difference between the current output signal and the last trigger instant, ΩyIs an event trigger matrix, and Ωy>0，ey(T) is the error, χ, between the current output signal of the T-S fuzzy model and the last transmitted output signaly(t) is satisfying xy(t)∈(0,1]Variable trigger threshold of yT() means for outputting a vector for the current system, y (-) is the current system output vector;
wherein the error e of the current output signal of the T-S fuzzy model and the last transmitted output signaly(t) is:
Figure FDA0003570880930000046
variable trigger threshold χy(t) satisfies:
Figure FDA0003570880930000047
wherein the content of the first and second substances,
Figure FDA0003570880930000048
in order for the event to trigger the threshold value,
Figure FDA0003570880930000049
for adjusting the variable trigger threshold χy(t) a parameter of convergence speed;
observer input signal based on adaptive event trigger mechanism
Figure FDA00035708809300000410
Comprises the following steps:
Figure FDA00035708809300000411
wherein d (t) is a time-varying function,
Figure FDA00035708809300000412
Figure FDA00035708809300000413
in order to be able to process the time interval,
Figure FDA00035708809300000414
epsilon is an intermediate parameter which is,
Figure FDA00035708809300000415
tau is the maximum network-induced time delay,
Figure FDA00035708809300000416
τyis a time of day
Figure FDA00035708809300000417
The maximum network at the sensor end of (1) induces a time delay,
Figure FDA00035708809300000418
is a time of day
Figure FDA00035708809300000419
The maximum network at the observer end of (1) induces a time delay.
3. The double-ended event triggered nonlinear control method according to claim 2, characterized in that a second event generator is provided in the observer to controller data transmission channel;
in the data transmission channel from the observer to the controller, at the moment of application of the trigger
Figure FDA00035708809300000420
Observer end
Observer state signal transmitted by second event generator
Figure FDA0003570880930000051
At the time of day
Figure FDA0003570880930000052
The data center of the controller is reached through a transmission network;
Figure FDA0003570880930000053
is a time of day
Figure FDA0003570880930000054
The maximum network at the observer end of (1) induces a time delay,
Figure FDA0003570880930000055
Figure FDA0003570880930000056
is a time of day
Figure FDA0003570880930000057
The maximum network induced delay of the observer end;
the second event generator reduces the network redundancy of the state signal under the condition of an adaptive event triggering mechanism according to the state information of the observer, wherein the condition of the adaptive triggering mechanism is as follows:
Figure FDA0003570880930000058
in the formula (I), the compound is shown in the specification,
Figure FDA0003570880930000059
for the moment when the next event trigger occurs,
Figure FDA00035708809300000510
as a transpose of the difference of the current observer state and the last triggered observer state,
Figure FDA00035708809300000511
in order to trigger the matrix for an event,
Figure FDA00035708809300000512
the difference between the current observer state and the last triggered observer state,
Figure FDA00035708809300000513
to adapt the trigger threshold of the trigger mechanism,
Figure FDA00035708809300000514
is a transpose of the state vector of the current observer,
Figure FDA00035708809300000515
is the current observer state vector;
wherein the trigger threshold of the adaptive trigger mechanism
Figure FDA00035708809300000516
Satisfies the following conditions:
Figure FDA00035708809300000517
in the formula (I), the compound is shown in the specification,
Figure FDA00035708809300000518
in order for the event to trigger the threshold value,
Figure FDA00035708809300000519
for adjusting trigger threshold
Figure FDA00035708809300000520
A parameter of convergence speed;
input signal of controller based on adaptive event trigger mechanism
Figure FDA00035708809300000521
Comprises the following steps:
Figure FDA00035708809300000522
4. the double-ended event triggered nonlinear control method according to claim 3, wherein the step S2 is specifically:
s21, defining a Lyapunov function containing event triggering information;
s22, carrying out derivation on the Lyapunov function, and eliminating an integral term in the Lyapunov function to obtain the stability condition of the closed-loop model of the nonlinear networked control system;
s23, eliminating a nonlinear term in the stability condition of the nonlinear networked control system closed-loop model;
and S24, on the basis of the stability condition of the networked control system closed-loop model with the nonlinear term eliminated, obtaining a system stability condition related to the membership function based on the linear matrix inequality by using the time delay information of the membership function, and taking the system stability condition as the final stability condition of the nonlinear networked control system closed-loop model.
5. The double-ended event triggered nonlinear control method according to claim 4, wherein the Lyapunov function in step S21 is:
V(t)＝V1(t)+V2(t)+V3(t)
in the formula (I), the compound is shown in the specification,
Figure FDA0003570880930000061
Figure FDA0003570880930000062
Figure FDA0003570880930000063
wherein ξT(t) is the transpose of the coupling vector for observed state change rate and state error, ξ (t) is the coupling vector for observed state change rate and state error, P, Q, R and W are both matrices with appropriate dimensions, ikh is an intermediate parameter which is a function of,
Figure FDA0003570880930000064
dMis the upper delay bound.
6. The double-ended event triggered nonlinear control method according to claim 5, wherein in step S22, when d is satisfiedM>0，
Figure FDA0003570880930000065
And
Figure FDA0003570880930000066
if there is a matrix P with suitable dimensions>0，Q>0，R>0，W>0，Ωy>0，
Figure FDA00035708809300000613
And U, the stability condition of the closed-loop model of the nonlinear networked control system is as follows:
Figure FDA0003570880930000067
Figure FDA0003570880930000068
in the formula phiijlIn order to determine the matrix for the stability,
Figure FDA0003570880930000069
and
Figure FDA00035708809300000610
are all phiijlIs an element of
Figure FDA00035708809300000611
A transposed matrix of mid-diagonal positions;
wherein
Figure FDA00035708809300000612
Figure FDA0003570880930000071
Figure FDA0003570880930000072
F＝[Aijl Lijl 0 Leijl Leyj]
In the formula (I), the compound is shown in the specification,
Figure FDA0003570880930000073
and
Figure FDA0003570880930000074
are all made of
Figure FDA0003570880930000075
The element in (1), col {. is a column matrix, diag {. is a block diagonal matrix, and F is an intermediate matrix;
and is
Figure FDA0003570880930000076
Figure FDA0003570880930000077
Figure FDA0003570880930000078
Figure FDA0003570880930000079
Figure FDA00035708809300000710
In the formula, CiAre all system matrices, G ═ I I]，E＝[I 0]And I is an identity matrix of a suitable dimension.
7. The double-ended event triggered nonlinear control method according to claim 6, characterized in that the stepsIn step S24, when d isM>0，
Figure FDA00035708809300000711
And
Figure FDA00035708809300000712
and the membership function of the fuzzy controller satisfies
Figure FDA00035708809300000713
Wherein 0<ιl≤1，ιlFor appropriate constants, if any, with matrix X of appropriate dimensions>0，
Figure FDA00035708809300000714
Figure FDA00035708809300000715
TjAnd YlThen, the system stability condition related to the membership function based on the linear matrix inequality is:
Figure FDA00035708809300000716
Figure FDA00035708809300000717
Figure FDA00035708809300000718
Figure FDA00035708809300000719
wherein the content of the first and second substances,
Figure FDA00035708809300000720
iota discrimination matrix for stability after elimination of non-linear termsjIs less than iotalIs suitably constant;
Figure FDA0003570880930000081
Figure FDA0003570880930000082
Figure FDA0003570880930000083
Figure FDA0003570880930000084
Figure FDA0003570880930000085
Figure FDA0003570880930000086
Figure FDA0003570880930000087
Figure FDA0003570880930000088
Figure FDA0003570880930000089
Figure FDA00035708809300000810
Figure FDA00035708809300000811
Figure FDA00035708809300000812
Figure FDA00035708809300000813
Figure FDA00035708809300000814
Figure FDA00035708809300000815
Figure FDA00035708809300000816
Figure FDA00035708809300000817
Figure FDA0003570880930000091
Figure FDA0003570880930000092
Figure FDA0003570880930000093
Figure FDA0003570880930000094
Figure FDA0003570880930000095
Figure FDA0003570880930000096
Figure FDA0003570880930000097
Figure FDA0003570880930000098
in the formula (I), the compound is shown in the specification,
Figure FDA0003570880930000099
and
Figure FDA00035708809300000910
is composed of
Figure FDA00035708809300000911
Of (2).
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